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Beijing University of Chemical Technology

Academic institutionasia · cn
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Research library67linked papers
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Selected work

Representative Papers

PBD-AG: Persistent Baseline-Delta Active Graphs with Uncertainty-Aware Inspection for Long-Horizon Service Robots

Aug 11, 2026

This work addresses the challenge of constructing a persistent, autonomously updatable, and geometrically verifiable world model for long-term service robots operating in unknown environments—a task hindered by error accumulation, static scene representations, and insufficient 3D geometric evidence in existing approaches. The authors propose a baseline-increment decoupled active graph framework that separates stable static structures from revisable dynamic objects, building a structural baseline through autonomous exploration and performing uncertainty-aware verification via hierarchical object beliefs. A novel reliability-weighted state representation and a geometry-aware visibility gating mechanism are introduced to jointly inform graph-conditioned viewpoint planning, effectively mitigating erroneous deletions under occlusion and enhancing object identity continuity and event recall. Experiments demonstrate significant improvements over baselines in multi-environment simulations, with superior performance in static-object F1 scores, identity continuity, and event recall, and successful integration with onboard systems is validated on a physical robot.

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ColorFD: A Finite-Difference Guided Black-Box Physical Adversarial Attack for Remote Sensing Object Detection

Aug 05, 2026

This work addresses the poor physical feasibility and inefficient high-dimensional optimization inherent in black-box physical adversarial attacks for remote sensing object detection by proposing ColorFD, a novel method that employs solid-color patches as physical perturbations. ColorFD jointly optimizes patch location and color via differential evolution, significantly reducing the search space through an innovative integration of finite-difference-guided critical region localization and class-level spatial priors. Furthermore, it introduces a target-aware fitness mechanism to enhance both attack specificity and transferability. Experimental results demonstrate that ColorFD consistently outperforms existing black-box approaches across YOLOv3u, YOLOv5u, and Faster R-CNN detectors, achieving performance close to white-box baselines, with digital-domain optimizations effectively transferring to real-world imaging conditions.

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Recent publications

Latest Papers

PBD-AG: Persistent Baseline-Delta Active Graphs with Uncertainty-Aware Inspection for Long-Horizon Service Robots

Aug 11, 2026

This work addresses the challenge of constructing a persistent, autonomously updatable, and geometrically verifiable world model for long-term service robots operating in unknown environments—a task hindered by error accumulation, static scene representations, and insufficient 3D geometric evidence in existing approaches. The authors propose a baseline-increment decoupled active graph framework that separates stable static structures from revisable dynamic objects, building a structural baseline through autonomous exploration and performing uncertainty-aware verification via hierarchical object beliefs. A novel reliability-weighted state representation and a geometry-aware visibility gating mechanism are introduced to jointly inform graph-conditioned viewpoint planning, effectively mitigating erroneous deletions under occlusion and enhancing object identity continuity and event recall. Experiments demonstrate significant improvements over baselines in multi-environment simulations, with superior performance in static-object F1 scores, identity continuity, and event recall, and successful integration with onboard systems is validated on a physical robot.

0 citationsRead paper

ColorFD: A Finite-Difference Guided Black-Box Physical Adversarial Attack for Remote Sensing Object Detection

Aug 05, 2026

This work addresses the poor physical feasibility and inefficient high-dimensional optimization inherent in black-box physical adversarial attacks for remote sensing object detection by proposing ColorFD, a novel method that employs solid-color patches as physical perturbations. ColorFD jointly optimizes patch location and color via differential evolution, significantly reducing the search space through an innovative integration of finite-difference-guided critical region localization and class-level spatial priors. Furthermore, it introduces a target-aware fitness mechanism to enhance both attack specificity and transferability. Experimental results demonstrate that ColorFD consistently outperforms existing black-box approaches across YOLOv3u, YOLOv5u, and Faster R-CNN detectors, achieving performance close to white-box baselines, with digital-domain optimizations effectively transferring to real-world imaging conditions.

0 citationsRead paper